5,436 research outputs found
Inferring Narrative Causality between Event Pairs in Films
To understand narrative, humans draw inferences about the underlying
relations between narrative events. Cognitive theories of narrative
understanding define these inferences as four different types of causality,
that include pairs of events A, B where A physically causes B (X drop, X
break), to pairs of events where A causes emotional state B (Y saw X, Y felt
fear). Previous work on learning narrative relations from text has either
focused on "strict" physical causality, or has been vague about what relation
is being learned. This paper learns pairs of causal events from a corpus of
film scene descriptions which are action rich and tend to be told in
chronological order. We show that event pairs induced using our methods are of
high quality and are judged to have a stronger causal relation than event pairs
from Rel-grams
Using Element Clustering to Increase the Efficiency of XML Schema Matching
Schema matching attempts to discover semantic mappings between elements of two schemas. Elements are cross compared using various heuristics (e.g., name, data-type, and structure similarity). Seen from a broader perspective, the schema matching problem is a combinatorial problem with an exponential complexity. This makes the naive matching algorithms for large schemas prohibitively inefficient. In this paper we propose a clustering based technique for improving the efficiency of large scale schema matching. The technique inserts clustering as an intermediate step into existing schema matching algorithms. Clustering partitions schemas and reduces the overall matching load, and creates a possibility to trade between the efficiency and effectiveness. The technique can be used in addition to other optimization techniques. In the paper we describe the technique, validate the performance of one implementation of the technique, and open directions for future research
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